{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/code/create-image-lists","entry":"create_image_lists","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":5,"n_papers_ran":0,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":5,"n_samples_ran":0,"n_samples_fingerprinted":0,"n_places":5,"n_places_pointer_only":1,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":0,"unverified":5},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"1807.04720","paper":"/paper/the-gan-landscape-losses-architectures","title":"A Large-Scale Study on Regularization and Normalization in GANs","date":"2018-07-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"w510056105/DeepLearning","path":"Projects/TransferLearning/InceptionV3_TransferLearning.py","file_url":"https://github.com/w510056105/DeepLearning/blob/HEAD/Projects/TransferLearning/InceptionV3_TransferLearning.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"af5a5c1e5be254a9","mcp_get_code":{"code_sha256":"af5a5c1e5be254a9"}},{"arxiv_id":"1804.09337","paper":"/paper/learning-a-discriminative-feature-network-for","title":"Learning a Discriminative Feature Network for Semantic Segmentation","date":"2018-04-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YuhuiMa/DFN-tensorflow","path":"utils.py","file_url":"https://github.com/YuhuiMa/DFN-tensorflow/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"aac3c56d9f630559","mcp_get_code":{"code_sha256":"aac3c56d9f630559"}},{"arxiv_id":"1701.07875","paper":"/paper/wasserstein-gan","title":"Wasserstein GAN","date":"2017-01-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shekkizh/WassersteinGAN.tensorflow","path":"Dataset_Reader/read_celebADataset.py","file_url":"https://github.com/shekkizh/WassersteinGAN.tensorflow/blob/HEAD/Dataset_Reader/read_celebADataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b5c1644a78d48827","mcp_get_code":{"code_sha256":"b5c1644a78d48827"}},{"arxiv_id":"1603.08511","paper":"/paper/colorful-image-colorization","title":"Colorful Image Colorization","date":"2016-03-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"BerenLuthien/HyperColumns_ImageColorization","path":"read_LaMemDataset.py","file_url":"https://github.com/BerenLuthien/HyperColumns_ImageColorization/blob/HEAD/read_LaMemDataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"8600a5e9c2d1e012","mcp_get_code":{"code_sha256":"8600a5e9c2d1e012"}},{"arxiv_id":"1512.00567","paper":"/paper/rethinking-the-inception-architecture-for","title":"Rethinking the Inception Architecture for Computer Vision","date":"2015-12-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"athulyashyju/sample-project","path":"train_data.py","file_url":"https://github.com/athulyashyju/sample-project/blob/HEAD/train_data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":false,"code_sha256_prefix":"8fcfb76b09017d43","mcp_get_code":{"code_sha256":"8fcfb76b09017d43"}}]}